**4. Proposed Deep Model**

We propose a deep model that has dual-channel inputs. One is raw data, and the other contains the six tuples of statistical components as we defined above. The overall architecture of the proposed deep model for electricity consumption forecasting can be seen from Figure 2 and a detailed configuration of the proposed deep model is shown in Table 2.

**Figure 2.** The architecture of the proposed multi-channels and scales convolutional neural networks (MCSCNN)–LSTM at three levels.


**Table 2.** Detailed configuration information of the proposed deep model.

Modifying the hyperparameters such as number and size of filter can improve the performance of the model. We defined the configuration information of MCSCNN–LSTM empirically. Here, we defined *H*, *D*, *W*, *M* as 24. The filter numbers of CNN decrease from 16 to 10 due to the shallow CNN layer being in charge of the detailed local feature extraction; the deeper CNN layer functions to capture abstract global feature representations. At the same time, LSTM is relatively time-consuming, so we defined proper output nodes in two LSTM layers as 20 and 10, respectively. From Figure 2, we can see six parts in our MCSCNN–LSTM: Input, CNN feature extraction, LSTM feature extraction, feature fusion, output, and weights updating. Every part is explained in detail as follows.
